Stanford RAIN/OR (Recent advances in AI, INcentives, and Operations Research) is a seminar on the theory and practice of AI, incentives, and operations research. It serves as a hub for talks and discussion at the intersection of these fields and society. It is supported by Stanford’s Society & Algorithms Lab (SOAL), Stanford OpenLab, the Stanford Center for Computational Market Design, and the Stanford Computer Forum (and is open to Computer Forum affiliates).
Autumn 2026 talks are held in person on Tuesdays from 4:30–5:30 PM PT; room assignments are listed below.
Recent Results on Contest Theory and its Modern Implications
Talk details
The theory of contest design, introduced nearly half a century ago, has been a central problem in economics, computer science, and operations research, with numerous applications including research contests, sports tournaments, crowdsourcing, and a wide range of managerial scenarios. However, classic results often impose crucial restrictions on the objective function, such as convexity, which severely restricts their applicability beyond simplistic scenarios. This talk will introduce recent results in the theory of contest design that significantly generalize prior work and provide a unified structural and algorithmic framework, motivated by modern online platforms and recommender systems. Then I will discuss their potential implications in the modern agentic economy and large language models.
Bio: Suho Shin is a joint postdoctoral scholar at Stanford (Motwani Postdoctoral Fellow) and MIT, hosted by Prof. Amin Saberi and Prof. Negin Golrezaei. He received his Ph.D. in Computer Science from the University of Maryland advised by Prof. MohammadTaghi Hajiaghayi, and B.S. and M.S. in Mathematics and Electrical Engineering from KAIST. His research lies at the intersection of mechanism design, principal-agent problems, and the modern digital/agentic economy. His recent works focused on designing robust delegation protocols that efficiently align incentives for both human and AI agents.
Justin Whitehouse
Stanford University
4:30–5:30 PM PT·Y2E2 299
Measuring Gift Card Program Incrementality via Causal Data Fusion
Talk details
Businesses regularly offer gift card programs to drive customer spending and increase engagement. A central question is how much incremental revenue these programs generate, and which distribution channels drive it most efficiently. Measuring the incremental revenue associated with a gift card program is a challenging problem in causal inference, requiring a firm to infer how much each customer would have spent if they never received a gift card. Observational data on past customer purchasing behavior reveal possession of a gift card only when a customer makes a purchase, thus leaving a customer’s treatment status systematically censored.
In this talk, we develop a novel data fusion approach to overcome this missing data challenge. We identify and estimate incrementality by combining a large observational dataset with a smaller experimental dataset from a different population. Our approach relies on a mild transferability condition, which posits that the conditional relative treatment effect of gift card receipt on the decision to purchase is invariant across the two populations. We develop a flexible, machine learning-based estimator for the incremental revenue and establish its asymptotic normality.
We apply our estimator across both first- and third-party channels through which Airbnb distributes gift cards, finding heterogeneity in incrementality across segments of the population. In particular, we find not only that third-party channels are more incremental than first-party ones but also that “self-gifters” (i.e., customers likely to have purchased their own gift cards) are more incremental than the broader population.
Bio: Justin Whitehouse is a SAIL Postdoctoral Fellow at Stanford University, where he works with Vasilis Syrgkanis, Ramesh Johari, and Emma Brunskill. His research lies at the intersection of causal inference, machine learning, and statistics, with a particular focus on developing methods for optimal and personalized decision-making. His work spans statistical theory and application, often motivated by decision-making problems that arise in industry. Prior to Stanford, Justin received his PhD in Computer Science from Carnegie Mellon University, where he was advised by Aaditya Ramdas and Steven Wu. His doctoral research focused on anytime-valid statistical inference, with applications across machine learning and causal inference.
